{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple Real Business Cycle Model\n", "\n", "This notebook contains the example code from \"State Space Estimation of Time Series Models in Python: Statsmodels\" for the simple RBC model." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# These are the basic import statements to get the required Python functionality\n", "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data\n", "\n", "For this example, we consider an RBC model in which output, labor, and consumption are the observable series." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# RBC model\n", "from pandas_datareader.data import DataReader\n", "start = '1984-01'\n", "end = '2016-09'\n", "labor = DataReader('HOANBS', 'fred',start=start, end=end).resample('QS').first()\n", "cons = DataReader('PCECC96', 'fred', start=start, end=end).resample('QS').first()\n", "inv = DataReader('GPDIC1', 'fred', start=start, end=end).resample('QS').first()\n", "pop = DataReader('CNP16OV', 'fred', start=start, end=end)\n", "pop = pop.resample('QS').mean() # Convert pop from monthly to quarterly observations\n", "recessions = DataReader('USRECQ', 'fred', start=start, end=end)\n", "recessions = recessions.resample('QS').last()['USRECQ'].iloc[1:]\n", "\n", "# Get in per-capita terms\n", "N = labor['HOANBS'] * 6e4 / pop['CNP16OV']\n", "C = (cons['PCECC96'] * 1e6 / pop['CNP16OV']) / 4\n", "I = (inv['GPDIC1'] * 1e6 / pop['CNP16OV']) / 4\n", "Y = C + I\n", "\n", "# Log, detrend\n", "y = np.log(Y).diff()[1:]\n", "c = np.log(C).diff()[1:]\n", "n = np.log(N).diff()[1:]\n", "i = np.log(I).diff()[1:]\n", "rbc_data = pd.concat((y, n, c), axis=1)\n", "rbc_data.columns = ['output', 'labor', 'consumption']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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09mF+sDBw3L8nh1vk0aM2kkIUfDs/YNLvaJlPD6IoMuCEsIDRn53dLbLluJVl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aysNBLpU8nmrzeTqE3kq5yv2NQi3o+vasS535xtJis3i8kH1mHxS2bf1t4JabhiqU2nJCurTXL3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\nnlMUJQIocTKsAIhpdz/ats1+jNuA7wHzV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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the series\n", "fig, ax = plt.subplots(figsize=(13, 3), dpi=300)\n", "\n", "ax.plot(y.index, y, label=r'Output $(y_t)$')\n", "ax.plot(n.index, n, label=r'Labor $(n_t)$')\n", "ax.plot(c.index, c, label=r'Consumption $(c_t)$')\n", "\n", "ax.yaxis.grid()\n", "ax.legend(loc='lower left', labelspacing=0.3);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## RBC Model\n", "\n", "The simple RBC model considered here can be found in Ruge-Murcia (2007) or Dejong and Dave (2011). The non-linear system of equilibrium conditions is as follows:\n", "\n", "$$\n", "\\begin{align}\n", "\\psi c_t & = (1 - \\alpha) z_t \\left ( \\frac{k_t}{n_t} \\right )^{\\alpha} & \\text{Static FOC} \\\\\n", "\\frac{1}{c_t} & = \\beta E_t \\left \\{ \\frac{1}{c_{t+1}} \\left [ \\alpha z_{t+1} \\left ( \\frac{k_{t+1}}{n_{t+1}} \\right )^{\\alpha - 1} + (1 - \\delta) \\right ] \\right \\} & \\text{Euler equation} \\\\\n", "y_t & = z_t k_t^\\alpha n_t^{1 - \\alpha} & \\text{Production function} \\\\\n", "y_t & = c_t + i_t & \\text{Aggregate resource constraint} \\\\\n", "k_{t+1} & = (1 - \\delta) k_t + i_t & \\text{Captial accumulation} \\\\\n", "1 & = l_t + n_t & \\text{Labor-leisure tradeoff} \\\\\n", "\\log z_t & = \\rho \\log z_{t-1} + \\varepsilon_t & \\text{Technology shock transition}\n", "\\end{align}\n", "$$\n", "\n", "by linearizing the model around the non-stochastic steady state, reducing the system to the three variables above, and solving with the method of Blanchard and Kahn (1980), we achieve a model in state space form.\n", "\n", "The class ``SimpleRBC``, below, implements the log-linearization step in the ``log_linearize`` method and the Blanchard-Kahn solution in the ``solve`` method. The ``params`` vector for that class is the vector of *structural* parameters:\n", "\n", "$$\n", "\\begin{align}\n", "(& \\beta, & \\text{Discount rate}\\\\\n", "& \\psi, & \\text{Marginal disutility of labor}\\\\\n", "& \\delta, & \\text{Depreciation rate}\\\\\n", "& \\alpha, & \\text{Capital-share of output}\\\\\n", "& \\rho, & \\text{Technology shock persistence}\\\\\n", "& \\sigma^2 & \\text{Technology shock variance} )\n", "\\end{align}$$\n", "\n", "Also, the class below has a number of parameter transformations to ensure valid parameters. For example, ``transform_discount_rate`` takes as its argument a parameter that can vary over the entire real line and transformed it to lie in the set $(0, 1)$. This can be convenient when performing maximum likelihood estimation with some numerical optimization routines." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## State space form\n", "\n", "After log-linearizing and solving the model, the resultant state space form is:\n", "\n", "$$\n", "\\begin{align}\n", "\\begin{bmatrix} y_t \\\\ n_t \\\\ c_t \\end{bmatrix} & = \\underbrace{\\begin{bmatrix}\n", " \\phi_{yk} & \\phi_{yz} \\\\\n", " \\phi_{nk} & \\phi_{nz} \\\\\n", " \\phi_{ck} & \\phi_{cz} \\\\\n", " \\end{bmatrix}}_{Z} \\underbrace{\\begin{bmatrix} k_t \\\\ z_t \\end{bmatrix}}_{\\alpha_t} +\n", " \\underbrace{\\begin{bmatrix} \\varepsilon_{y,t} \\\\ \\varepsilon_{n,t} \\\\ \\varepsilon_{c,t} \\end{bmatrix}}_{\\varepsilon_t}, \\qquad \\varepsilon_t \\sim N \\left ( \\begin{bmatrix} 0 \\\\ 0 \\\\ 0\\end{bmatrix}, \\begin{bmatrix}\n", " \\sigma_{y}^2 & 0 & 0 \\\\\n", " 0 & \\sigma_{n}^2 & 0 \\\\\n", " 0 & 0 & \\sigma_{c}^2 \\\\\n", " \\end{bmatrix} \\right ) \\\\\n", " \\begin{bmatrix} k_{t+1} \\\\ z_{t+1} \\end{bmatrix} & = \\underbrace{\\begin{bmatrix}\n", " T_{kk} & T_{kz} \\\\\n", " 0 & \\rho\n", " \\end{bmatrix}}_{T} \\begin{bmatrix} k_t \\\\ z_t \\end{bmatrix} +\n", " \\underbrace{\\begin{bmatrix} 0 \\\\ 1 \\end{bmatrix}}_{R}\n", " \\eta_t, \\qquad \\eta_t \\sim N(0, \\sigma_z^2)\n", "\\end{align}\n", "$$\n", "\n", "where the reduced form parameters in the state space model are non-linear functions of the parameters in the RBC model, above.\n", "\n", "The ``update`` method is called with a ``params`` vector holding the structural parameters. By log-linearizing and solving the model, those sturctural parameters are transformed into the reduced form parameters making up the state space form, and these are placed into the ``design``, ``obs_cov``, ``transition``, and ``state_cov`` matrices. Then the Kalman filter and smoother can be applied to retrieve smoothed estimates of the unobserved states, compute the log-likelihood for maximum likelihood estimation, or as part of the simulation smoother for Bayesian estimation." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from collections import OrderedDict\n", "class SimpleRBC(sm.tsa.statespace.MLEModel):\n", "\n", " parameters = OrderedDict([\n", " ('discount_rate', 0.95),\n", " ('disutility_labor', 3.),\n", " ('depreciation_rate', 0.025),\n", " ('capital_share', 0.36),\n", " ('technology_shock_persistence', 0.85),\n", " ('technology_shock_var', 0.04**2)\n", " ])\n", "\n", " def __init__(self, endog, calibrated=None):\n", " super(SimpleRBC, self).__init__(\n", " endog, k_states=2, k_posdef=1, initialization='stationary')\n", " self.k_predetermined = 1\n", "\n", " # Save the calibrated vs. estimated parameters\n", " parameters = self.parameters.keys()\n", " calibrated = calibrated or {}\n", " self.calibrated = OrderedDict([\n", " (param, calibrated[param]) for param in parameters\n", " if param in calibrated\n", " ])\n", " self.idx_calibrated = np.array([\n", " param in self.calibrated for param in parameters])\n", " self.idx_estimated = ~self.idx_calibrated\n", "\n", " self.k_params = len(self.parameters)\n", " self.k_calibrated = len(self.calibrated)\n", " self.k_estimated = self.k_params - self.k_calibrated\n", "\n", " self.idx_cap_share = parameters.index('capital_share')\n", " self.idx_tech_pers = parameters.index('technology_shock_persistence')\n", " self.idx_tech_var = parameters.index('technology_shock_var')\n", "\n", " # Setup fixed elements of system matrices\n", " self['selection', 1, 0] = 1\n", "\n", " @property\n", " def start_params(self):\n", " structural_params = np.array(self.parameters.values())[self.idx_estimated]\n", " measurement_variances = [0.1] * 3\n", " return np.r_[structural_params, measurement_variances]\n", "\n", " @property\n", " def param_names(self):\n", " structural_params = np.array(self.parameters.keys())[self.idx_estimated]\n", " measurement_variances = ['%s.var' % name for name in self.endog_names]\n", " return structural_params.tolist() + measurement_variances\n", "\n", " def log_linearize(self, params):\n", " # Extract the parameters\n", " (discount_rate, disutility_labor, depreciation_rate, capital_share,\n", " technology_shock_persistence, technology_shock_var) = params\n", "\n", " # Temporary values\n", " tmp = (1. / discount_rate - (1. - depreciation_rate))\n", " theta = (capital_share / tmp)**(1. / (1. - capital_share))\n", " gamma = 1. - depreciation_rate * theta**(1. - capital_share)\n", " zeta = capital_share * discount_rate * theta**(capital_share - 1)\n", "\n", " # Coefficient matrices from linearization\n", " A = np.eye(2)\n", "\n", " B11 = 1 + depreciation_rate * (gamma / (1 - gamma))\n", " B12 = (-depreciation_rate *\n", " (1 - capital_share + gamma * capital_share) /\n", " (capital_share * (1 - gamma)))\n", " B21 = 0\n", " B22 = capital_share / (zeta + capital_share*(1 - zeta))\n", " B = np.array([[B11, B12], [B21, B22]])\n", "\n", " C1 = depreciation_rate / (capital_share * (1 - gamma))\n", " C2 = (zeta * technology_shock_persistence /\n", " (zeta + capital_share*(1 - zeta)))\n", " C = np.array([[C1], [C2]])\n", "\n", " return A, B, C\n", "\n", " def solve(self, params):\n", " capital_share = params[self.idx_cap_share]\n", " technology_shock_persistence = params[self.idx_tech_pers]\n", "\n", " # Get the coefficient matrices from linearization\n", " A, B, C = self.log_linearize(params)\n", "\n", " # Jordan decomposition of B\n", " eigvals, right_eigvecs = np.linalg.eig(np.transpose(B))\n", " left_eigvecs = np.transpose(right_eigvecs)\n", "\n", " # Re-order, ascending\n", " idx = np.argsort(eigvals)\n", " eigvals = np.diag(eigvals[idx])\n", " left_eigvecs = left_eigvecs[idx, :]\n", "\n", " # Blanchard-Kahn conditions\n", " k_nonpredetermined = self.k_states - self.k_predetermined\n", " k_stable = len(np.where(eigvals.diagonal() < 1)[0])\n", " k_unstable = self.k_states - k_stable\n", " if not k_stable == self.k_predetermined:\n", " raise RuntimeError('Blanchard-Kahn condition not met.'\n", " ' Unique solution does not exist.')\n", "\n", " # Create partition indices\n", " k = self.k_predetermined\n", " p1 = np.s_[:k]\n", " p2 = np.s_[k:]\n", "\n", " p11 = np.s_[:k, :k]\n", " p12 = np.s_[:k, k:]\n", " p21 = np.s_[k:, :k]\n", " p22 = np.s_[k:, k:]\n", "\n", " # Decouple the system\n", " decoupled_C = np.dot(left_eigvecs, C)\n", "\n", " # Solve the explosive component (controls) in terms of the\n", " # non-explosive component (states) and shocks\n", " tmp = np.linalg.inv(left_eigvecs[p22])\n", "\n", " # This is \\phi_{ck}, above\n", " policy_state = - np.dot(tmp, left_eigvecs[p21]).squeeze()\n", " # This is \\phi_{cz}, above\n", " policy_shock = -(\n", " np.dot(tmp, 1. / eigvals[p22]).dot(\n", " np.linalg.inv(\n", " np.eye(k_nonpredetermined) -\n", " technology_shock_persistence / eigvals[p22]\n", " )\n", " ).dot(decoupled_C[p2])\n", " ).squeeze()\n", "\n", " # Solve for the non-explosive transition\n", " # This is T_{kk}, above\n", " transition_state = np.squeeze(B[p11] + np.dot(B[p12], policy_state))\n", " # This is T_{kz}, above\n", " transition_shock = np.squeeze(np.dot(B[p12], policy_shock) + C[p1])\n", "\n", " # Create the full design matrix\n", " tmp = (1 - capital_share) / capital_share\n", " tmp1 = 1. / capital_share\n", " design = np.array([[1 - tmp * policy_state, tmp1 - tmp * policy_shock],\n", " [1 - tmp1 * policy_state, tmp1 * (1-policy_shock)],\n", " [policy_state, policy_shock]])\n", "\n", " # Create the transition matrix\n", " transition = (\n", " np.array([[transition_state, transition_shock],\n", " [0, technology_shock_persistence]]))\n", "\n", " return design, transition\n", "\n", " def transform_discount_rate(self, param, untransform=False):\n", " # Discount rate must be between 0 and 1\n", " epsilon = 1e-4 # bound it slightly away from exactly 0 or 1\n", " if not untransform:\n", " return np.abs(1 / (1 + np.exp(param)) - epsilon)\n", " else:\n", " return np.log((1 - param + epsilon) / (param + epsilon))\n", "\n", " def transform_disutility_labor(self, param, untransform=False):\n", " # Disutility of labor must be positive\n", " return param**2 if not untransform else param**0.5\n", "\n", " def transform_depreciation_rate(self, param, untransform=False):\n", " # Depreciation rate must be positive\n", " return param**2 if not untransform else param**0.5\n", "\n", " def transform_capital_share(self, param, untransform=False):\n", " # Capital share must be between 0 and 1\n", " epsilon = 1e-4 # bound it slightly away from exactly 0 or 1\n", " if not untransform:\n", " return np.abs(1 / (1 + np.exp(param)) - epsilon)\n", " else:\n", " return np.log((1 - param + epsilon) / (param + epsilon))\n", "\n", " def transform_technology_shock_persistence(self, param, untransform=False):\n", " # Persistence parameter must be between -1 and 1\n", " if not untransform:\n", " return param / (1 + np.abs(param))\n", " else:\n", " return param / (1 - param)\n", "\n", " def transform_technology_shock_var(self, unconstrained, untransform=False):\n", " # Variances must be positive\n", " return unconstrained**2 if not untransform else unconstrained**0.5\n", "\n", " def transform_params(self, unconstrained):\n", " constrained = np.zeros(unconstrained.shape, unconstrained.dtype)\n", "\n", " i = 0\n", " for param in self.parameters.keys():\n", " if param not in self.calibrated:\n", " method = getattr(self, 'transform_%s' % param)\n", " constrained[i] = method(unconstrained[i])\n", " i += 1\n", "\n", " # Measurement error variances must be positive\n", " constrained[self.k_estimated:] = unconstrained[self.k_estimated:]**2\n", "\n", " return constrained\n", "\n", " def untransform_params(self, constrained):\n", " unconstrained = np.zeros(constrained.shape, constrained.dtype)\n", "\n", " i = 0\n", " for param in self.parameters.keys():\n", " if param not in self.calibrated:\n", " method = getattr(self, 'transform_%s' % param)\n", " unconstrained[i] = method(constrained[i], untransform=True)\n", " i += 1\n", "\n", " # Measurement error variances must be positive\n", " unconstrained[self.k_estimated:] = constrained[self.k_estimated:]**0.5\n", "\n", " return unconstrained\n", "\n", " def update(self, params, **kwargs):\n", " params = super(SimpleRBC, self).update(params, **kwargs)\n", "\n", " # Reconstruct the full parameter vector from the\n", " # estimated and calibrated parameters\n", " structural_params = np.zeros(self.k_params, dtype=params.dtype)\n", " structural_params[self.idx_calibrated] = self.calibrated.values()\n", " structural_params[self.idx_estimated] = params[:self.k_estimated]\n", " measurement_variances = params[self.k_estimated:]\n", "\n", " # Solve the model\n", " design, transition = self.solve(structural_params)\n", "\n", " # Update the statespace representation\n", " self['design'] = design\n", " self['obs_cov', 0, 0] = measurement_variances[0]\n", " self['obs_cov', 1, 1] = measurement_variances[1]\n", " self['obs_cov', 2, 2] = measurement_variances[2]\n", " self['transition'] = transition\n", " self['state_cov', 0, 0] = structural_params[self.idx_tech_var]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calibration / Maximum likelihood estimation\n", "\n", "It is sometimes interesting to calibrate the structural parameters of the model, and estimate only the measurement error variables." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Statespace Model Results \n", "==============================================================================================\n", "Dep. Variable: ['output', 'labor', 'consumption'] No. Observations: 130\n", "Model: SimpleRBC Log Likelihood 1221.724\n", "Date: Sat, 28 Jan 2017 AIC -2437.447\n", "Time: 14:20:15 BIC -2428.845\n", "Sample: 04-01-1984 HQIC -2433.952\n", " - 07-01-2016 \n", "Covariance Type: opg \n", "===================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-----------------------------------------------------------------------------------\n", "output.var 1.902e-11 6.65e-06 2.86e-06 1.000 -1.3e-05 1.3e-05\n", "labor.var 3.928e-05 4.54e-06 8.660 0.000 3.04e-05 4.82e-05\n", "consumption.var 1.516e-05 1.83e-06 8.287 0.000 1.16e-05 1.87e-05\n", "=====================================================================================\n", "Ljung-Box (Q): 54.96, 147.38, 50.79 Jarque-Bera (JB): 8.58, 8.46, 7.85\n", "Prob(Q): 0.06, 0.00, 0.12 Prob(JB): 0.01, 0.01, 0.02\n", "Heteroskedasticity (H): 1.09, 1.12, 0.52 Skew: -0.19, -0.51, 0.54\n", "Prob(H) (two-sided): 0.77, 0.71, 0.03 Kurtosis: 4.20, 3.72, 3.51\n", "=====================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n" ] } ], "source": [ "# Calibrate everything except measurement variances\n", "calibrated = {\n", " 'discount_rate': 0.95,\n", " 'disutility_labor': 3.0,\n", " 'capital_share': 0.36,\n", " 'depreciation_rate': 0.025,\n", " 'technology_shock_persistence': 0.85,\n", " 'technology_shock_var': 0.04**2\n", "}\n", "calibrated_mod = SimpleRBC(rbc_data, calibrated=calibrated)\n", "calibrated_res = calibrated_mod.fit(method='nm', maxiter=1000, disp=0)\n", "\n", "calibrated_irfs = calibrated_res.impulse_responses(40, orthogonalized=True) * 100\n", "print(calibrated_res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Output plots\n", "\n", "Because we're going to be making the same plots for a couple of examples, we define a function here to do that for us." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from scipy.stats import norm\n", "\n", "def plot_irfs(irfs):\n", " fig, ax = plt.subplots(figsize=(13, 2), dpi=300)\n", "\n", " lines, = ax.plot(irfs['output'], label='')\n", " ax.plot(irfs['output'], 'o', label='Output', color=lines.get_color(),\n", " markersize=4, alpha=0.8)\n", " lines, = ax.plot(irfs['labor'], label='')\n", " ax.plot(irfs['labor'], '^', label='Labor', color=lines.get_color(),\n", " markersize=4, alpha=0.8)\n", " lines, = ax.plot(irfs['consumption'], label='')\n", " ax.plot(irfs['consumption'], 's', label='Consumption',\n", " color=lines.get_color(), markersize=4, alpha=0.8)\n", "\n", " ax.hlines(0, 0, irfs.shape[0], alpha=0.9, linestyle=':', linewidth=1)\n", " ylim = ax.get_ylim()\n", " ax.vlines(0, ylim[0]+1e-6, ylim[1]-1e-6, alpha=0.9, linestyle=':',\n", " linewidth=1)\n", " [ax.spines[spine].set(linewidth=0) for spine in ['top', 'right']]\n", " ax.set(xlabel='Quarters after impulse', ylabel='Impulse response (\\%)',\n", " xlim=(-1, len(irfs)))\n", "\n", " ax.legend(labelspacing=0.3)\n", " \n", " return fig\n", "\n", "def plot_states(res):\n", " fig, ax = plt.subplots(figsize=(13, 3), dpi=300)\n", "\n", " alpha = 0.1\n", " q = norm.ppf(1 - alpha / 2)\n", "\n", " capital = res.smoothed_state[0, :]\n", " capital_se = res.smoothed_state_cov[0, 0, :]**0.5\n", " capital_lower = capital - capital_se * q\n", " capital_upper = capital + capital_se * q\n", "\n", " shock = res.smoothed_state[1, :]\n", " shock_se = res.smoothed_state_cov[1, 1, :]**0.5\n", " shock_lower = shock - shock_se * q\n", " shock_upper = shock + shock_se * q\n", "\n", " line_capital, = ax.plot(rbc_data.index, capital, label='Capital')\n", " ax.fill_between(rbc_data.index, capital_lower, capital_upper, alpha=0.25,\n", " color=line_capital.get_color())\n", "\n", " line_shock, = ax.plot(rbc_data.index, shock, label='Technology process')\n", " ax.fill_between(rbc_data.index, shock_lower, shock_upper, alpha=0.25,\n", " color=line_shock.get_color())\n", "\n", " ax.hlines(0, rbc_data.index[0], rbc_data.index[-1], 'k')\n", " ax.yaxis.grid()\n", "\n", " ylim = ax.get_ylim()\n", " ax.fill_between(recessions.index, ylim[0]+1e-5, ylim[1]-1e-5, recessions,\n", " facecolor='k', alpha=0.1)\n", "\n", " p1 = plt.Rectangle((0, 0), 1, 1, fc=\"grey\", alpha=0.3)\n", " ax.legend([line_capital, line_shock, p1],\n", " [\"Capital\", \"Technology process\", \"NBER recession indicator\"],\n", " loc='lower left');\n", " \n", " return fig" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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gZiJYArq8zOeML++F7R9C1Gi48/sOOWR5jYOZL6ygoMrFi7N7kxRpPeO2Pep6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QkRyQJ6eFlVY0ofY4wREbseWnEauMEYUywiw4E3RWSIMeabKzMaY34L/BZcz5kYP358jfUFBQVE\nRka6P0dERNhURRfPbdclPDycHTt2sGLFCqKjowkPD6eiooLIyEjCw8P5yU9+wk9/+lMuX75McnIy\nBw4c4LbbbiM0NJRVq1Zx33331dheu3btSElJYd26dRQXFzNgwAAefPBBrr/++jrrUFlZSdu2TT95\n1759+2zZTnp6eoPLiAgdO3YkMjLS73rs3LmT0NBQ94P1fHFlrMvLywkLC2PIkCHuNJ2PXKnG0+dM\nKF/485wJAIriCD/jpCq8Mz0Gj6HH9wZcNbseZypY1TvMyRhzhzFmqJfXNuAfItITwHo/42UTfwc8\nf+X2stKu9je/NcYUW8v7gSNAf992qflp27Ytc+fOZfXq1VfNV15eTllZGV26eL2P3atu3brRr18/\nTp8+XWvdU089xcyZMxk9ejQzZ86kqqqK1NRUbr75ZhISEvjNb37jzpuenk58fDyJiYksWeK69eXI\nkSNMnDiR4cOHM3bsWA4fPgzAli1bGDp0KImJiSQnJwOQn5/PiBEjcDgcJCQkUFRUBEDHjh0B19n5\n1NRUhg4dSnx8PJs3bwZg165djB8/nvvuu4+BAwcyY8YMvD1Icfbs2WzduhWA3r17s2LFCoYNG0Z8\nfLy7XsXFxUyYMIEhQ4bw8MMP19hOdT3q2tcNGzZw8803k5iYyL333ktpaSl79+5l+/btpKam4nA4\nOHLkCE6nk1tuuYWEhASmTJnCV199BcD48eNZtGgRSUlJvPjiiz7/+ymllGqeqjr3AeBSz1ugW/2j\nEJQKVo29Z2I7MMtangVs85InG7hJROJEJAxIscrVSUS6WzduIyJ9gJuAo42sa0A99thjZGZmcv78\n+VrrVq9ejcPhoGfPnvTv3x+H47txmdU/ZB0OBzNmzKhV9sSJE5SVlZGQkOD17x46dIisrCxee+01\nMjIy6NSpE9nZ2WRnZ7NhwwaOHTvGn/70J7Zt28a+ffvIzc3lZz/7GQBz585l7dq17N+/n1WrVrFg\nwQIAnnnmGXbu3Elubi7bt7v+KdevX8/jjz+O0+kkJyeHXr161ajHG2+8gdPpJDc3l6ysLFJTU90d\noI8//pgXXniBQ4cOcfToUfbs2VNvPKOjozlw4ADz589n1apVADz99NOMGTOG/Px8pkyZwokTJ2qV\nq2tfp06dSnZ2Nrm5uQwaNIiMjAxGjRrF5MmT+dWvfoXT6aRv37489NBDpKenk5eXR3x8PE8//bR7\n2+Xl5eTk5LB48eJ666+UUqp5q+zsesp16XWjIEQfy6VUXRrbmfglcKeIFAF3WJ8RkVgR+SOAMaYS\nWAjsBAqA/zHG5Fv5pojIF8CtwP+JyE5ru8lAnog4ga3APGPMuUbWNaCioqJ46KGHWLNmTa11Tzzx\nBE6nkzNnznDx4kVef/1197rqH7JOp5PMzEx3+ubNm0lISKBfv34sWLCAdu3aef27kydPdg/t+stf\n/sLLL7+Mw+Fg5MiRFBcXU1RURFZWFnPmzKF9+/YAdO3alZKSEvbu3cv999+Pw+Hg0Ucfdf/4Hz16\nNLNnz2bDhg1UVVUBcOutt7Jy5UrS09P5/PPPaw0ne//995k+fTohISHExMQwbtw4srOzARgxYgS9\nevWiTZs2OBwOjh8/Xm88p06dCsDw4cPd+Xfv3s2DDz4IwN133+31Co+3fQU4ePAgY8eOJT4+nszM\nzBr3N1Q7f/48X3/9NePGjQNg1qxZ7N793URk06ZNq7feSimlWoaLg6dRfOsvqIy7PdBVUapZa1Rn\nwhhTbIy53RhzkzUc6pyVfsoYM8kj3x+NMf2NMX2NMc95pP+vMaaXMSbcGBNjjLnLSv+DMWaINS3s\nMGPMW42pZ3OxaNEiMjIyuHjxotf1oaGhTJw4scYP1LpMmzaNvLw89u7dy5IlS/jyyy+95uvQoYN7\n2RjD2rVr3Z2TY8eOMWHCBK/lLl++TOfOnd15nU4nBQUFgOsqRFpaGidPnmT48OEUFxfzwAMPsH37\ndiIiIpg0aRLvvPNOvftQLTw83L0cEhJCZWWlz2V8zV+f2bNns27dOj755BNWrFhBWVlZg7fhGWul\nlFItmwmL5Nv4B6B910BXRalmTZ+A3YS6du3KD3/4QzIyMryuN8awZ88en2aIqpaUlMTMmTN9Gqd/\n11138dJLL7lntigsLOTixYvceeedbNy4kdLSUgDOnTtHVFQUcXFxbNmyxV233NxcwHUvxciRI3nm\nmWfo3r07J0+e5OjRo/Tp04cf//jH3HPPPeTl5dX422PHjmXz5s1UVVVx9uxZdu/ezYgRI3zeT18k\nJyfz6quvAq7hTNX3M3jytq8AFy5coGfPnlRUVNS4AhQZGemeDaxTp0506dKF995zTSz2yiuvuK9S\nKKWUUkoFo6AbBDhgwNVnY7jWFi9ezLp162qkrV69mt///vdUVFSQkJDgvjcBXPdMpKWluT9/9NFH\ntbb585//nGHDhrF06dKrzi718MMPc/z4cYYNG4Yxhu7du/Pmm28yceJEnE4nSUlJhIWFMWnSJFau\nXElmZibz588nLS2NiooKUlJSSExMJDU1laKiIowx3H777SQmJpKens4rr7xCaGgoPXr0YOnSpTX+\n9pQpU/jggw9ITExERHj++efp0aOH++ZpO6xYsYLp06czZMgQRo0axQ033FArT137+uyzzzJy5Ei6\nd+/OyJEj3R2IlJQUHnnkEdasWcPWrVvZtGkT8+bNo7S0lD59+rBx40bb6q+UUkop1dKIt5lzWqqk\npCSTk5NTI62goIBBgwYFqEZKuW7MLiwsdN+fATqFoFJ20KlhlS/8nhq2gfQ4U62NiOw3xiTVl0+H\nOSmllFJKKaX8EnTDnJRqamFhYYSEhOhZK6Vspt8p5Qs9TpS6toLiykRrGsqlWh49/pRSSinVWrX6\nzkS7du0oLi7WH3QqIIwxFBcX1/kcEKWUUkqplqzVD3Pq1asXX3zxBWfPng10VVSQateuXa0ngiul\nlFJKtQatvjMRGhpKXFxcoKuhlFJKKaVUq9PqhzkppZRSSimlrg3tTCillFJKKaX8op0JpZRSSiml\nlF9a1ROwReQs8PlVskQD/2yi6rRmGsfG0xjaQ+NoD42jPTSO9tA4Np7G0B7BHscbjTHd68vUqjoT\n9RGRHF8eC66uTuPYeBpDe2gc7aFxtIfG0R4ax8bTGNpD4+gbHeaklFJKKaWU8ot2JpRSSimllFJ+\nCbbOxG8DXYFWQuPYeBpDe2gc7aFxtIfG0R4ax8bTGNpD4+iDoLpnQimllFJKKWWfYLsyoZRSSiml\nlLKJdiaUUkoppZRSfmnRnQkR+Z2InBGRgx5piSLygYh8IiJviUiUlR4qIpus9AIRedKjzJ9FJFdE\n8kVkvYiEBGJ/AsWOOIpIpIg4PV7/FJEXArVPgdDAOIaJyEYrPVdExnuUeU5ETopISQB2I+BsjOMu\nEfnU45j8XgB2JyBsjOE0Ecmz/m9MD8CuBJSIXC8i74rIISsGj1vpXUXkbREpst67eJR5UkQ+s469\nuzzSg7adsSuOwdzONDSGItLNyl8iIuuu2FbQtjE2xzFo25hajDEt9gUkA8OAgx5p2cA4a/lHwLPW\n8gPA69Zye+A40Nv6HGW9C/AHICXQ+9YS43jFNvcDyYHet2Ycx8eAjdby96x4tbE+3wL0BEoCvU8t\nPI67gKRA709LjSHQDTgBdLfWbQJuD/S+NXEcewLDrOVIoBAYDDwPLLHSlwDp1vJgIBcIB+KAI0CI\ntS5o2xk743jFdoOmnfEjhh2AMcA8YN0V2wraNsbmOAZtG3Plq0VfmTDG7AbOXZHcH9htLb8N3Fud\nHeggIm2BCKAc+MbazjdWnrZAmJU3aNgVx2oi0h/Xj5L3rlWdm6MGxnEw8I5V7gzwNZBkff7QGHP6\nmle4mbIrjsHMphj2AYqMMWetfFkeZYKCMea0MeaAtXwBKACuA+7B1bnCev+BtXwPrpMt3xpjjgGf\nASOs8kHbztgZx2rB1s40NIbGmIvGmPeBMi/bCto2xs44qu+06M5EHfJxHRQA9wPXW8tbgYvAaVxn\n21YZY9yNrYjsBM4AF6y8wc6vOFpSgM3G6roHubrimAtMFpG2IhIHDPdYp2rzN44brcvPvxARabrq\nNksNjeFnwAAR6W2dPPgBQXyMikhv4F+AfUCMx4+xL4EYa/k64KRHsS+stOptBH07Y0ccLUHbzvgY\nQ1UPm+KobQytszPxI2CBiOzHdQmr3EofAVQBsbgumy4WkT7VhYwxd+G6/BUO/GuT1rh58iuOlhTg\ntaaqaDNXVxx/h6uBzAFeAPbiiqvyzp84zjDGxANjrdfMJq1x89OgGBpjvgLmA5txnf09TpAeoyLS\nEdfQpEUeVxgAsH7M+vSDNtjbGbviaAnKdsbmGAYtm+KobYylbaArYDdjzGFgArgvg95trXoA+LMx\npgI4IyJ7cF3KP+pRtkxEtuE6e/d2k1a8mfE3jiKSCLQ1xuxv+lo3P3XF0RhTCTxRnU9E9uIau6m8\n8CeOxpi/W+8XRORVXB3hl5u25s2HnzF8C3jLSp9LEHYmRCQU14+OTGPMG1byP0SkpzHmtIj0xHW1\nAeDv1Lx608tKcwvWdsbOOAZrO9PAGKo62BVHbWO+0+quTFTfTS8ibYDlwHpr1QmsM0Ei0gHXDUiH\nRaSjdeBgXcq/Gzjc1PVubhoaR4+i0wnCs0V1qSuOItLeih8icidQaYw5FLCKNnMNjaM1ZCfaSg8F\n/g046HXjQcKfY9GjTBdgAfBfAah6wFjDFjKAAmPMf3is2g7MspZnAds80lNEJNwaMnYT8FGwtzN2\nxdGjXNC1M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HEJuEAUAp5QN6aa23tjaq1jouxzA0pKIAnp0MFfkQqoDUfnD+/dD/7GMeSvZ7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\nSlV9hRugPfgs11DpdYjXaOnnBmjn9YXc3u4HoC3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_states(calibrated_res);\n", "plot_irfs(calibrated_irfs);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Maximum likelihood estimation\n", "\n", "We can try to estimate more parameters." ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Statespace Model Results \n", "==============================================================================================\n", "Dep. Variable: ['output', 'labor', 'consumption'] No. Observations: 130\n", "Model: SimpleRBC Log Likelihood 1494.643\n", "Date: Sat, 28 Jan 2017 AIC -2979.285\n", "Time: 14:27:04 BIC -2964.948\n", "Sample: 04-01-1984 HQIC -2973.459\n", " - 07-01-2016 \n", "Covariance Type: opg \n", "================================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------------\n", "technology_shock_persistence 0.9372 0.022 42.304 0.000 0.894 0.981\n", "technology_shock_var 5.772e-06 1.02e-06 5.641 0.000 3.77e-06 7.78e-06\n", "output.var 1.054e-05 2.59e-06 4.062 0.000 5.45e-06 1.56e-05\n", "labor.var 3.41e-05 4.51e-06 7.568 0.000 2.53e-05 4.29e-05\n", "consumption.var 1.483e-05 1.68e-06 8.839 0.000 1.15e-05 1.81e-05\n", "=====================================================================================\n", "Ljung-Box (Q): 39.52, 198.09, 50.97 Jarque-Bera (JB): 10.74, 6.94, 15.37\n", "Prob(Q): 0.49, 0.00, 0.11 Prob(JB): 0.00, 0.03, 0.00\n", "Heteroskedasticity (H): 1.28, 1.15, 0.54 Skew: 0.06, -0.50, 0.69\n", "Prob(H) (two-sided): 0.43, 0.64, 0.05 Kurtosis: 4.40, 3.52, 3.96\n", "=====================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n" ] } ], "source": [ "# Now, don't calibrate the technology parameters\n", "partially_calibrated = {\n", " 'discount_rate': 0.95,\n", " 'disutility_labor': 3.0,\n", " 'capital_share': 0.33,\n", " 'depreciation_rate': 0.025,\n", "}\n", "partial_mod = SimpleRBC(rbc_data, calibrated=partially_calibrated)\n", "partial_res = partial_mod.fit(method='nm', maxiter=1000, disp=0)\n", "\n", "partial_irfs = partial_res.impulse_responses(40, orthogonalized=True) * 100\n", "print(partial_res.summary())" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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KpbqCnudc2eWpk5PUREM4XsD/+MeXaU9F+YE76xBCqLQ3ITm4tXHZyfOmOt9y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qiFxAHhIEKAIQQCCJoQbH8zy6DH54kurAg6OKcqjyS7RYNpsH+HLl+6lOq3EiJ+j44BsXgfqSaH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\nIiIikhF1JkREREREJCP/B153mS1Ku7XSAAAAAElFTkSuQmCC\n", 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aVker2/1m3m13E3AG8Dv9BFwBAs5Au9s/WPoDgq5g9sG9Pl7P/13xfzhsjuxD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\neGN47S54888hzOrCXekefTOvmzUewa7tMOmWmC6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_states(partial_res);\n", "plot_irfs(partial_irfs);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Metropolis-within-Gibbs Sampling" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000\n", "2000\n", "3000\n", "4000\n", "5000\n", "6000\n", "7000\n", "8000\n", "9000\n", "10000\n" ] } ], "source": [ "def draw_posterior_rho(model, states, sigma2, truncate=False):\n", " Z = states[1:2, 1:]\n", " X = states[1:2, :-1]\n", "\n", " tmp = 1 / (sigma2 + np.sum(X**2))\n", " post_mean = tmp * np.squeeze(np.dot(X, Z.T))\n", " post_var = tmp * sigma2\n", "\n", " if truncate:\n", " lower = (-1 - post_mean) / post_var**0.5\n", " upper = (1 - post_mean) / post_var**0.5\n", " rvs = truncnorm.rvs(lower, upper, loc=post_mean, scale=post_var**0.5)\n", " else:\n", " rvs = norm.rvs(post_mean, post_var**0.5)\n", " return rvs\n", "\n", "def draw_posterior_sigma2(model, states, rho):\n", " resid = states[1, 1:] - rho * states[1, :-1]\n", " post_shape = 2.00005 + model.nobs\n", " post_scale = 0.0100005 + np.sum(resid**2)\n", "\n", " return invgamma.rvs(post_shape, scale=post_scale)\n", "\n", "np.set_printoptions(suppress=True)\n", "np.random.seed(17429)\n", "\n", "from statsmodels.tsa.statespace.tools import is_invertible\n", "from scipy.stats import multivariate_normal, gamma, invgamma, beta, uniform\n", "\n", "# Create the model for likelihood evaluation\n", "calibrated = {\n", " 'disutility_labor': 3.0,\n", " 'depreciation_rate': 0.025,\n", "}\n", "model = SimpleRBC(rbc_data, calibrated=calibrated)\n", "sim_smoother = model.simulation_smoother()\n", "\n", "# Specify priors\n", "prior_discount = gamma(6.25, scale=0.04)\n", "prior_cap_share = norm(0.3, scale=0.01)\n", "prior_meas_err = invgamma(2.0025, scale=0.10025)\n", "\n", "# Proposals\n", "rw_discount = norm(scale=0.3)\n", "rw_cap_share = norm(scale=0.01)\n", "rw_meas_err = norm(scale=0.003)\n", "\n", "# Create storage arrays for the traces\n", "n_iterations = 10000\n", "trace = np.zeros((n_iterations + 1, 7))\n", "trace_accepts = np.zeros((n_iterations, 5))\n", "trace[0] = model.start_params\n", "trace[0, 0] = 100 * ((1 / trace[0, 0]) - 1)\n", "\n", "loglike = None\n", "\n", "# Iterations\n", "for s in range(1, n_iterations + 1):\n", " if s % 1000 == 0:\n", " print s\n", " # Get the parameters from the trace\n", " discount_rate = 1 / (1 + (trace[s-1, 0] / 100))\n", " capital_share = trace[s-1, 1]\n", " rho = trace[s-1, 2]\n", " sigma2 = trace[s-1, 3]\n", " meas_vars = trace[s-1, 4:]**2\n", "\n", " # 1. Gibbs step: draw the states using the simulation smoother\n", " model.update(np.r_[discount_rate, capital_share, rho, sigma2, meas_vars])\n", " sim_smoother.simulate()\n", " states = sim_smoother.simulated_state[:, :-1]\n", "\n", " # 2. Gibbs step: draw the autoregressive parameter, and apply\n", " # rejection sampling to ensure an invertible lag polynomial\n", " # In rare cases due to the combinations of other parameters,\n", " # the mean of the normal posterior will be greater than one\n", " # and it becomes difficult to draw from a normal distribution\n", " # even with rejection sampling. In those cases we draw from a\n", " # truncated normal.\n", " rho = draw_posterior_rho(model, states, sigma2)\n", " i = 0\n", " while rho < -1 or rho > 1:\n", " if i < 1e2:\n", " rho = draw_posterior_rho(model, states, sigma2)\n", " else:\n", " rho = draw_posterior_rho(model, states, sigma2, truncate=True)\n", " i += 1\n", " trace[s, 2] = rho\n", "\n", " # 3. Gibbs step: draw the variance parameter\n", " sigma2 = draw_posterior_sigma2(model, states, rho)\n", " trace[s, 3] = sigma2\n", "\n", " # Calculate the loglikelihood\n", " loglike = model.loglike(np.r_[discount_rate, capital_share, rho, sigma2, meas_vars])\n", "\n", " # 4. Metropolis-step for the discount rate\n", " discount_param = trace[s-1, 0]\n", " proposal_param = discount_param + rw_discount.rvs()\n", " proposal_rate = 1 / (1 + (proposal_param / 100))\n", " if proposal_rate < 1:\n", " proposal_loglike = model.loglike(np.r_[proposal_rate, capital_share, rho, sigma2, meas_vars])\n", " acceptance_probability = np.exp(\n", " proposal_loglike - loglike +\n", " prior_discount.logpdf(proposal_param) -\n", " prior_discount.logpdf(discount_param))\n", "\n", " if acceptance_probability > uniform.rvs():\n", " discount_param = proposal_param\n", " discount_rate = proposal_rate\n", " loglike = proposal_loglike\n", " trace_accepts[s-1, 0] = 1\n", "\n", " trace[s, 0] = discount_param\n", "\n", " # 5. Metropolis-step for the capital-share\n", " proposal = capital_share + rw_cap_share.rvs()\n", " if proposal > 0 and proposal < 1:\n", " proposal_loglike = model.loglike(np.r_[discount_rate, proposal, rho, sigma2, meas_vars])\n", " acceptance_probability = np.exp(\n", " proposal_loglike - loglike +\n", " prior_cap_share.logpdf(proposal) -\n", " prior_cap_share.logpdf(capital_share))\n", "\n", " if acceptance_probability > uniform.rvs():\n", " capital_share = proposal\n", " trace_accepts[s-1, 1] = 1\n", " loglike = proposal_loglike\n", " trace[s, 1] = capital_share\n", "\n", " # 6. Metropolis-step for the measurement errors\n", " for i in range(3):\n", " meas_std = meas_vars[i]**0.5\n", " proposal = meas_std + rw_meas_err.rvs()\n", " proposal_vars = meas_vars.copy()\n", " proposal_vars[i] = proposal**2\n", " if proposal > 0:\n", " proposal_loglike = model.loglike(np.r_[discount_rate, capital_share, rho, sigma2, proposal_vars])\n", " acceptance_probability = np.exp(\n", " proposal_loglike - loglike +\n", " prior_meas_err.logpdf(proposal) -\n", " prior_meas_err.logpdf(meas_std))\n", "\n", " if acceptance_probability > uniform.rvs():\n", " meas_std = proposal\n", " trace_accepts[s-1, 2+i] = 1\n", " loglike = proposal_loglike\n", " meas_vars[i] = proposal_vars[i]\n", " trace[s, 4+i] = meas_std\n" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Mode Mean 5 percent 95 percent\n", "0 0.997 0.996 0.994 0.998\n", "1 0.329 0.326 0.309 0.344\n", "2 0.647 0.624 0.269 0.946\n", "3 8.21e-05 8.3e-05 7.07e-05 9.52e-05\n", "4 2.01e-05 2.11e-05 1.35e-05 2.97e-05\n", "5 2.93e-05 3.02e-05 2.18e-05 4.09e-05\n", "6 2.44e-05 2.45e-05 1.86e-05 3.15e-05\n" ] } ], "source": [ "from scipy.stats import gaussian_kde\n", "\n", "burn = 1000\n", "thin = 10\n", "\n", "final_trace = trace.copy()\n", "final_trace = final_trace[burn:][::thin]\n", "final_trace[:, 0] = 1 / (1 + (final_trace[:, 0] / 100))\n", "final_trace[:, 4:] = final_trace[:, 4:]**2\n", "\n", "modes = np.zeros(7)\n", "means = np.mean(final_trace, axis=0)\n", "discount_kde = gaussian_kde(final_trace[:, 0])\n", "cap_share_kde = gaussian_kde(final_trace[:, 1])\n", "rho_kde = gaussian_kde(final_trace[:, 2])\n", "sigma2_kde = gaussian_kde(final_trace[:, 3])\n", "\n", "# Finish calculating modes\n", "for i in range(7):\n", " kde = gaussian_kde(final_trace[:, i])\n", " X = np.linspace(np.min(final_trace[:, i]),\n", " np.max(final_trace[:, i]), 1000)\n", " Y = kde(X)\n", " modes[i] = X[np.argmax(Y)]\n", "\n", "test = pd.DataFrame(final_trace)\n", "\n", "print(pd.DataFrame(\n", " np.c_[modes, means, test.quantile(q=0.05), test.quantile(q=0.95)],\n", " columns=['Mode', 'Mean', '5 percent', '95 percent']\n", ").to_string(float_format=lambda x: '%.3g' % x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sampler output" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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2A5vwjxldgr8IbSAi+4D6gcdKZTxRF2FOB7A7oP0s/5rMCoBj5IbaA2D3Ivjz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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Trace plots\n", "fig, axes = plt.subplots(2, 2, figsize=(13, 5), dpi=300)\n", "\n", "axes[0, 0].hist(final_trace[:, 0], bins=20, normed=True, alpha=1)\n", "X = np.linspace(0.990, 1.0-1e-4, 1000)\n", "Y = discount_kde(X)\n", "modes[0] = X[np.argmax(Y)]\n", "line, = axes[0, 0].plot(X, Y)\n", "ylim = axes[0, 0].get_ylim()\n", "vline = axes[0, 0].vlines(means[0], ylim[0], ylim[1], linewidth=2)\n", "axes[0, 0].set(title=r'Discount rate $\\beta$')\n", "\n", "axes[0, 1].hist(final_trace[:, 1], bins=20, normed=True, alpha=1)\n", "X = np.linspace(0.280, 0.370, 1000)\n", "Y = cap_share_kde(X)\n", "modes[1] = X[np.argmax(Y)]\n", "axes[0, 1].plot(X, Y)\n", "ylim = axes[0, 1].get_ylim()\n", "vline = axes[0, 1].vlines(means[1], ylim[0], ylim[1], linewidth=2)\n", "axes[0, 1].set(title=r'Capital share $\\alpha$')\n", "\n", "axes[1, 0].hist(final_trace[:, 2], bins=20, normed=True, alpha=1)\n", "X = np.linspace(-0.2, 1-1e-4, 1000)\n", "Y = rho_kde(X)\n", "modes[2] = X[np.argmax(Y)]\n", "axes[1, 0].plot(X, Y)\n", "ylim = axes[1, 0].get_ylim()\n", "vline = axes[1, 0].vlines(means[2], ylim[0], ylim[1], linewidth=2)\n", "axes[1, 0].set(title=r'Technology shock persistence $\\rho$')\n", "\n", "axes[1, 1].hist(final_trace[:, 3], bins=20, normed=True, alpha=1)\n", "X = np.linspace(0.6e-4, 1.1e-4, 1000)\n", "Y = sigma2_kde(X)\n", "modes[3] = X[np.argmax(Y)]\n", "axes[1, 1].plot(X, Y)\n", "ylim = axes[1, 1].get_ylim()\n", "vline = axes[1, 1].vlines(means[3], ylim[0], ylim[1], linewidth=2)\n", "axes[1, 1].ticklabel_format(style='sci', scilimits=(-2, 2))\n", "axes[1, 1].set(title=r'Technology shock variance $\\sigma^2$')\n", "\n", "p1 = plt.Rectangle((0, 0), 1, 1, alpha=0.7)\n", "axes[0, 0].legend([p1, line, vline],\n", " [\"Histogram\", \"Gaussian KDE\", \"Sample mean\"],\n", " loc='upper left')\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model of the posterior median\n", "\n", "One way to explore the implications of the posterior is to example the model at the posterior mean or median." ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Statespace Model Results \n", "==============================================================================================\n", "Dep. Variable: ['output', 'labor', 'consumption'] No. Observations: 130\n", "Model: SimpleRBC Log Likelihood 1347.991\n", "Date: Sat, 28 Jan 2017 AIC -2681.982\n", "Time: 14:43:18 BIC -2661.910\n", "Sample: 04-01-1984 HQIC -2673.826\n", " - 07-01-2016 \n", "Covariance Type: opg \n", "================================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------------------------\n", "discount_rate 0.9966 0.008 121.189 0.000 0.980 1.013\n", "capital_share 0.3261 0.177 1.846 0.065 -0.020 0.672\n", "technology_shock_persistence 0.6360 0.393 1.617 0.106 -0.135 1.407\n", "technology_shock_var 8.265e-05 7.37e-05 1.121 0.262 -6.18e-05 0.000\n", "output.var 2.063e-05 2.3e-05 0.897 0.370 -2.45e-05 6.57e-05\n", "labor.var 2.978e-05 2.09e-05 1.422 0.155 -1.13e-05 7.08e-05\n", "consumption.var 2.434e-05 4.2e-06 5.795 0.000 1.61e-05 3.26e-05\n", "=====================================================================================\n", "Ljung-Box (Q): 39.55, 84.02, 57.85 Jarque-Bera (JB): 17.66, 0.06, 5.24\n", "Prob(Q): 0.49, 0.00, 0.03 Prob(JB): 0.00, 0.97, 0.07\n", "Heteroskedasticity (H): 1.17, 0.80, 0.65 Skew: -0.39, -0.05, -0.30\n", "Prob(H) (two-sided): 0.60, 0.48, 0.16 Kurtosis: 4.63, 3.03, 3.78\n", "=====================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n" ] } ], "source": [ "gibbs_res = model.smooth(np.median(final_trace, axis=0))\n", "print(gibbs_res.summary())\n", "\n", "gibbs_irfs = gibbs_res.impulse_responses(40, orthogonalized=True)*100" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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5bufsxWFeGclhCuOasMM48PfeH/DbL8/y+XfrmB09/MD9vTe0kNddn3LDpS7V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\n8krOPPNMAG699VYmTZoEwMyZM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Gf1btj2yCQgghhBCiVbTWnH/++Zx22mns3LmTNWvW8Mgjj5CTk9PRqTWpbiFw7rnncu+9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\n8YfAibbflnQpcDHwky3cZ0REr5ZCICJi+3GmpPO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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_states(gibbs_res);\n", "plot_irfs(gibbs_irfs);" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }